In [4]:
sns.lineplot(data=mlo, x='month', y='c02', hue='year')
plt.legend(bbox_to_anchor=(1, 1))
plt.xlabel('Month')
plt.ylabel('CO2 fraction in dry air (ppm)')
plt.title('Monthly CO2 Cycle by Year')
plt.show()
This lab accompanies a lecture for UC Berkeley's Data 100 by Fernando Pérez and Dr. Chelle Gentemann that covers the fundamental physical mechanisms behind global warming and analyzes CO2 and ocean temperature data.
%matplotlib inline
import warnings
from zipfile import ZipFile
import numpy as np
import pandas as pd
import seaborn as sns
import xarray as xr
from IPython.display import HTML, Javascript, display
from matplotlib import pyplot as plt
from plotly import graph_objs as go
from plotly.offline import iplot
from plotly.subplots import make_subplots
from scipy.interpolate import CubicSpline
warnings.filterwarnings('ignore')
plt.rcParams['figure.figsize'] = (6, 4)
plt.rcParams['figure.dpi'] = 300
plt.rcParams['font.size'] = 8
plt.rcParams['figure.titlesize'] = 10
plt.rcParams['axes.linewidth'] = 0.1
plt.rcParams['patch.linewidth'] = 0
display(HTML("<style>.container { width:100% !important; }</style>"))
filename = 'https://scrippsco2.ucsd.edu/assets/data/atmospheric/stations/in_situ_co2/monthly/monthly_in_situ_co2_mlo.csv'
mlo = pd.read_csv(filename, skiprows=59, na_values=-99.99
).reset_index().dropna()
mlo.columns = ['year', 'month', 'date_index', 'fraction_date', 'c02',
'data_adjusted_season', 'data_fit',
'data_adjusted_seasonally_fit', 'data_filled',
'data_adjusted_seasonally_filed', 'station']
mlo.head(2)
| year | month | date_index | fraction_date | c02 | data_adjusted_season | data_fit | data_adjusted_seasonally_fit | data_filled | data_adjusted_seasonally_filed | station | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 2 | 1958 | 3 | 21259 | 1958.2027 | 315.71 | 314.44 | 316.19 | 314.90 | 315.71 | 314.44 | MLO |
| 3 | 1958 | 4 | 21290 | 1958.2877 | 317.45 | 315.16 | 317.30 | 314.98 | 317.45 | 315.16 | MLO |
trace1 = go.Scatter(x=mlo['fraction_date'], y=mlo['c02'])
trace2 = go.Scatter(x=mlo['fraction_date'],
y=mlo['data_adjusted_seasonally_fit'])
fig = make_subplots()
fig.add_trace(trace1)
fig.add_trace(trace2)
fig['layout'].update(height=600, width=700, showlegend=False,
title='Atmospheric CO2 at Mauna Loa Observatory',
xaxis_title='Year',
yaxis_title='CO2 fraction in dry air (ppm)')
iplot(fig)
# plt.plot(data=mlo, 'fraction_date', 'c02')
# plt.plot(data=mlo, 'fraction_date', 'data_adjusted_seasonally_fit')
# plt.title('Atmospheric CO2 at Mauna Loa Observatory')
# plt.xlabel('Year')
# plt.ylabel('CO2 fraction in dry air (ppm)')
# plt.show()
CO2 emission follows a similar monthly cycle each year. There is a slight increase around May and a dip around October.
sns.lineplot(data=mlo, x='month', y='c02', hue='year')
plt.legend(bbox_to_anchor=(1, 1))
plt.xlabel('Month')
plt.ylabel('CO2 fraction in dry air (ppm)')
plt.title('Monthly CO2 Cycle by Year')
plt.show()
annual = mlo.groupby('month').mean()
anomaly = annual - annual.mean()
def detrend(df):
temp = df - df.mean()
temp['month'] = df['month']
return temp
c02anomaly = mlo.drop(columns='station').groupby('year').apply(detrend)
monthly_anomaly = c02anomaly.groupby('month').mean()[['c02']]
# display(monthly_anomaly)
fig, ax = plt.subplots()
ax.plot(mlo['fraction_date'], mlo['data_filled'], 'r.', markersize=2)
ax.plot(mlo['fraction_date'], mlo['data_adjusted_seasonally_fit'], 'b')
ax.set_xlabel('Year')
ax.set_ylabel('CO2 fraction in dry air (ppm)')
ax.set_title('Monthly Mean CO2')
ax.grid(False)
axin1 = ax.inset_axes([0.1, 0.6, 0.3, 0.3])
cs = CubicSpline(monthly_anomaly.index.values, monthly_anomaly.c02)
xs = np.arange(1, 12.1, 0.1)
axin1.plot(monthly_anomaly.index, monthly_anomaly.c02, 'r.')
axin1.plot(xs, cs(xs))
axin1.set_xlabel('Month')
axin1.set_title('Seasonal Anomaly')
axin1.set_xticks(np.arange(2, 13, 2))
plt.show()
# files = ZipFile('data/era5_snow_monthly.nc.zip')
# ds = xr.open_dataset(files.open(files.namelist()[0]))
# mask = ds.lsm.mean('time') # {0: sea, 1: land, other: assume land}
# snow = ds.rsn.where(mask > 0, drop=True)
# snow = snow.interp(coords={'latitude': snow.latitude[0::7],
# 'longitude': snow.longitude[0::8]})
# snow.to_netcdf('data/era5_snow.nc')
# # snow
files = ZipFile('data/era5_snow.nc.zip')
snow = xr.open_dataset(files.open(files.namelist()[0])).rsn
snow
<xarray.DataArray 'rsn' (time: 120, latitude: 99, longitude: 180)>
[2138400 values with dtype=float32]
Coordinates:
* time (time) datetime64[ns] 2010-01-01 2010-02-01 ... 2019-12-01
* latitude (latitude) float32 83.75 82.0 80.25 78.5 ... -85.0 -86.75 -88.5
* longitude (longitude) float32 0.0 2.0 4.0 6.0 ... 352.0 354.0 356.0 358.0
Attributes:
units: kg m**-3
long_name: Snow densityfig, axs = plt.subplots(ncols=2, figsize=(15, 7))
xr.plot.hist(snow, color='c', alpha=0.4, ax=axs[0])
axs[0].set_title('Snow Density Distribution')
snow.mean('time').plot(ax=axs[1])
axs[1].set_title('Average Density')
plt.show()
There is more snow across the globe in February from snowfall in the winter season and less in August from warm weather in the summer. Greenland and Antarctica's snow density was similar both times of the year
fig, axs = plt.subplots(ncols=2, figsize=(15, 7))
snow.sel(time='2010-02').plot(ax=axs[0])
snow.sel(time='2010-08').plot(ax=axs[1])
plt.show()
On average, snow density peaks at around April and is lowest around July/August in the Northern Hemisphere. Snow density stays relatively constant throughout the year, on average.
by_month = snow.groupby('time.month').mean()
nn = by_month.where(snow.latitude > 0, drop=True).mean(
['latitude', 'longitude'])
ss = by_month.where(snow.latitude < 0, drop=True).mean(
['latitude', 'longitude'])
trace1 = go.Scatter(x=nn.month, y=nn, name='north')
trace2 = go.Scatter(x=ss.month, y=ss, name='south')
fig = make_subplots()
fig.add_trace(trace1)
fig.add_trace(trace2)
fig['layout'].update(height=600, width=600, showlegend=True,
title="Average Monthly Density in the 2010's",
xaxis_title='Month', yaxis_title='Snow Density')
iplot(fig)
# nn.plot(label='north')
# ss.plot(label='south')
# plt.legend()
# plt.show()
Max density in April and fluctuates throughout the years, while min in July/August stays constant
# get peaks for each year
def extract_peaks(da):
years = np.arange(2010, 2020).astype(str)
peaks = []
for y in years:
snow_sel = da.sel(time=y)
min_month = pd.to_datetime(snow_sel.idxmin().values).month
max_month = pd.to_datetime(snow_sel.idxmax().values).month
int(snow_sel.idxmin().dt.dayofyear)
int(snow_sel.idxmax().dt.dayofyear)
min_snow = float(snow_sel.min())
max_snow = float(snow_sel.max())
amplitude = max_snow - min_snow
peaks.append([y, min_month, max_month, min_snow, max_snow, amplitude])
return pd.DataFrame(peaks, columns=['year', 'min_month', 'max_month',
'min_snow', 'max_snow', 'amplitude'])
peaks = extract_peaks(snow.where(snow.latitude > 0, drop=True)
.mean(['latitude', 'longitude']))
display(peaks)
| year | min_month | max_month | min_snow | max_snow | amplitude | |
|---|---|---|---|---|---|---|
| 0 | 2010 | 7 | 4 | 112.835518 | 155.768860 | 42.933342 |
| 1 | 2011 | 7 | 4 | 113.086121 | 162.643204 | 49.557083 |
| 2 | 2012 | 7 | 4 | 112.561478 | 156.796539 | 44.235062 |
| 3 | 2013 | 8 | 4 | 113.702126 | 160.700287 | 46.998161 |
| 4 | 2014 | 8 | 4 | 112.960968 | 159.668961 | 46.707993 |
| 5 | 2015 | 7 | 4 | 112.696663 | 163.593735 | 50.897072 |
| 6 | 2016 | 7 | 4 | 112.435371 | 159.232452 | 46.797081 |
| 7 | 2017 | 8 | 4 | 113.219757 | 164.261642 | 51.041885 |
| 8 | 2018 | 8 | 4 | 112.922371 | 162.334503 | 49.412132 |
| 9 | 2019 | 8 | 4 | 112.321518 | 163.467422 | 51.145905 |
trace1 = go.Scatter(x=peaks.year, y=peaks.min_snow)
trace2 = go.Scatter(x=peaks.year, y=peaks.max_snow)
# trace3 = go.Bar(x=peaks.year, y=peaks.amplitude)
fig = make_subplots(specs=[[{'secondary_y': True}]])
fig.add_trace(trace1)
fig.add_trace(trace2)
# fig.add_trace(trace3,secondary_y=True)
fig['layout'].update(height=600, width=600, showlegend=False,
title="Average Monthly Density in the 2010's",
xaxis_title='Month', yaxis_title='Snow Density')
iplot(fig)
# plt.figure(figsize=(12, 12))
# plt.plot('year', 'min_snow', data=peaks)
# plt.plot('year', 'max_snow', data=peaks)
# plt.plot('year', 'min_snow', 'r.', color='black', data=peaks)
# plt.plot('year', 'max_snow', 'r.', color='black', data=peaks)
# plt.legend(['Minimum', 'Maximum'])
# plt.title('Min and Max Snow Accumulation')
# plt.xlabel('Year')
# plt.ylabel('Snow Density')
# plt.xticks(rotation=60)
# plt.show()
# save notebook
display(Javascript('IPython.notebook.save_checkpoint();'))
# save notebook as html to eugpoon.github.io/projects
!jupyter nbconvert climate.ipynb --to html
%mv "climate.html" "../eugpoon.github.io/projects/"
# restyle imports, clear output, replace file
!cleanipynb climate.ipynb
# restart kernel
display(HTML("<script>Jupyter.notebook.kernel.restart()</script>"))